CN113766426B - Early warning method and early warning device for vehicle running risk, electronic equipment and storage medium - Google Patents
Early warning method and early warning device for vehicle running risk, electronic equipment and storage medium Download PDFInfo
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- H04W4/02—Services making use of location information
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- H—ELECTRICITY
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- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
- H04W4/42—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for mass transport vehicles, e.g. buses, trains or aircraft
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Abstract
According to the early warning method, the early warning device, the electronic equipment and the storage medium for the vehicle running risk, the sampling track of the vehicle when running according to the preset track is obtained, the track correction is carried out on the sampling track, and the corrected track of the vehicle is obtained; determining the vehicle running error time according to the corrected track of the vehicle and the preset track of the vehicle; and early warning is carried out on the vehicle running risk according to the vehicle running error time, so that the real running track of the vehicle can be obtained, the running error time is determined based on the real running track of the vehicle, the early warning of the vehicle running risk is carried out, the early warning accuracy is further effectively improved, and the safety of the traffic environment is ensured.
Description
Technical Field
The present disclosure relates to the field of data processing, and in particular, to a method and system for early warning of vehicle driving risk, an electronic device, and a storage medium.
Background
The transportation of the dangerous chemical vehicles is an important component part of a traffic system, and the effective monitoring and early warning of the transportation safety of the dangerous chemical vehicles also becomes an indispensable link for ensuring traffic safety and civil safety. Due to the proliferation of fake plate vehicle phenomenon and bus private use phenomenon, how to identify and effectively early warn dangerous chemical vehicles with the phenomenon becomes a hot spot.
In the prior art, the early warning of the dangerous chemical vehicle is realized based on a GPS system arranged on the dangerous chemical vehicle, and the compliance and the safety of the vehicle transportation are ensured by acquiring the vehicle position reported by the GPS system and combining with a manual card setting mode to screen the vehicle transportation state.
However, in such a way, a large amount of human resources are required to be invoked to check one by one on site, and once the vehicle position reported by the GPS system is inaccurate, the accuracy of manual card setting can be seriously affected, the human resources are wasted, the accuracy of early warning is reduced, and the traffic safety hidden trouble is easy to occur.
Disclosure of Invention
The embodiment of the application provides a vehicle running risk early warning method, an early warning device, electronic equipment and a storage medium, so as to provide efficient and accurate early warning for vehicles with certain running risk such as dangerous chemical vehicles.
In one aspect, the present application provides a method for early warning of a running risk of a vehicle, including:
acquiring a sampling track when a vehicle runs according to a preset track, and carrying out track correction on the sampling track to obtain a corrected track of the vehicle;
determining the vehicle running error time according to the corrected track of the vehicle and the preset track of the vehicle;
And early warning the running risk of the vehicle according to the running error time of the vehicle.
In an alternative embodiment, the sampling trajectory includes trajectory positions of the vehicle at a plurality of sampling instants;
the track correction is performed on the sampling track to obtain a corrected track of the vehicle, and the method comprises the following steps:
and carrying out track correction on the sampling tracks according to the sampling time relations among the track positions to obtain corrected tracks of the vehicle.
In an optional embodiment, the performing track correction on the sampling track according to each sampling time relationship between each track position to obtain a corrected track of the vehicle includes:
determining correction weights of other track positions in the sampling track relative to the track position to be corrected according to the track position to be corrected in the sampling track, wherein the correction weights are used for representing the association degree of the other track positions and the track position to be corrected;
according to the correction weight and the position coordinates of each other track position, carrying out coordinate correction on the position coordinates of the track position to be corrected, and taking the corrected position coordinates as corrected track positions;
and finishing the correction of each track position in the sampling track, wherein each corrected track position forms a correction track of the vehicle.
In an alternative embodiment, the early warning method further includes:
establishing a weight function for determining the correction weight;
and determining the correction weight of other track positions in the sampling track relative to the track position to be corrected according to the weight function.
In an alternative embodiment, the early warning method further includes:
sequencing the track positions according to the sampling time of the track positions;
and sequentially selecting the track position with the earliest sampling time from the uncorrected track positions according to the sequence as the track position to be corrected, so as to carry out coordinate correction of the position coordinates on the track position to be corrected.
In an optional embodiment, the performing coordinate correction on the position coordinate of the track position to be corrected according to the correction weight and the position coordinate of each other track position further includes:
determining whether the position coordinates of each other track position are corrected;
if yes, adopting the corrected position coordinates and the correction weight to carry out coordinate correction on the position coordinates of the track position to be corrected.
In an alternative embodiment, the preset track includes a preset endpoint and a preset arrival time;
the determining the vehicle running error time according to the corrected track of the vehicle and the preset track of the vehicle comprises the following steps:
Determining the actual arrival time of the vehicle reaching a preset end point according to the corrected track of the vehicle;
and determining the vehicle running error time according to the actual arrival time and the preset arrival time.
In an alternative embodiment, the method further comprises:
repeatedly executing the steps of determining the actual arrival time of the vehicle reaching a preset end point according to the corrected track of the vehicle, and obtaining a plurality of actual arrival times of the vehicle;
the determining the vehicle driving error time according to the actual arrival time and the preset arrival time comprises the following steps:
calculating a difference between the actual arrival time and a preset arrival time for each determined actual arrival time of the vehicle;
the cumulative sum of the differences is taken as the vehicle running error time.
In an alternative embodiment, the determining, according to the corrected trajectory of the vehicle, an actual arrival time of the vehicle reaching a preset end point includes:
determining a current position of the vehicle in the corrected trajectory;
and determining the running time required by the vehicle to run from the current position to a preset end point along the preset track according to the preset track, wherein the running time is taken as the actual arrival time of the vehicle to the preset end point.
In an alternative embodiment, the method further comprises:
Determining whether the vehicle is positioned on the preset track according to the current position;
if not, reconstructing a preset track according to the current position and a preset end point;
correspondingly, determining the running time required by the vehicle to run from the current position to a preset end point along the preset track according to the preset track, wherein the running time is taken as the actual arrival time of the vehicle to the preset end point, and comprises the following steps:
and determining the running time required by the vehicle to run from the current position to a preset end point along the reconstructed preset track according to the reconstructed preset track, wherein the running time is taken as the actual arrival time of the vehicle to the preset end point.
In an alternative embodiment, the preset track includes a plurality of route points and corresponding preset route time;
the determining the vehicle running error time according to the corrected track of the vehicle and the preset track of the vehicle comprises the following steps:
determining the actual path time of the vehicle reaching each path point according to the corrected track of the vehicle;
and determining the vehicle running error time according to the actual route time and the preset route time of each route point.
In an alternative embodiment, the determining the vehicle driving error time according to the actual route time and the preset route time of each route point includes:
Determining the path time difference between the actual path time of each path point and each preset path time;
and taking the accumulated value of the time differences of each route as the running error time of the vehicle.
In an alternative embodiment, the early warning method further includes:
determining a geographical area through which the vehicle passes according to the corrected track of the vehicle;
and determining the regional information of the geographical region through which the vehicle passes so as to early warn the running risk of the vehicle.
In an alternative embodiment, the determining the area information of the geographical area through which the vehicle passes to early warn the running risk of the vehicle includes:
determining the regional function distribution of the geographic region according to the regional information of the geographic region;
determining risk coefficients of the geographic area according to the regional function distribution and a preset risk value corresponding to the regional function;
and pre-warning the running risk of the vehicle according to the risk coefficient of the geographic area.
In a second aspect, the present application provides a vehicle driving risk early warning device, including:
the track processing module is used for acquiring a sampling track when the vehicle runs according to a preset track, and carrying out track correction on the sampling track to obtain a corrected track of the vehicle;
the error time processing module is used for determining the running error time of the vehicle according to the corrected track of the vehicle and the preset track of the vehicle;
And the early warning module is used for early warning the running risk of the vehicle according to the running error time of the vehicle.
In an alternative embodiment, the early warning device further includes: a region information processing module;
the regional information processing module is used for determining the geographical region through which the vehicle passes according to the corrected track of the vehicle, and determining regional information of the geographical region through which the vehicle passes so as to enable the early warning module to early warn the running risk of the vehicle.
In a third aspect, the present application provides an electronic device, including: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executes computer-executable instructions stored in the memory, causing the at least one processor to perform the pre-warning method as described in the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium having stored therein computer-executable instructions that, when executed by a processor, implement the early warning method as in the first aspect.
According to the early warning method, the early warning device, the electronic equipment and the storage medium for the vehicle running risk, the sampling track of the vehicle when running according to the preset track is obtained, the track correction is carried out on the sampling track, and the corrected track of the vehicle is obtained; determining the vehicle running error time according to the corrected track of the vehicle and the preset track of the vehicle; and early warning is carried out on the vehicle running risk according to the vehicle running error time, so that the real running track of the vehicle can be obtained, the running error time is determined based on the real running track of the vehicle, the early warning of the vehicle running risk is carried out, the early warning accuracy is further effectively improved, and the safety of the traffic environment is ensured.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
FIG. 1 is a schematic diagram of a network architecture on which the present disclosure is based;
fig. 2 is a flow chart of a method for early warning of vehicle driving risk provided in the present application;
FIG. 3 is a schematic diagram of the track provided in the present embodiment;
fig. 4 is a flow chart of another method for early warning of vehicle driving risk according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an early warning device according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Specific embodiments thereof have been shown by way of example in the drawings and will herein be described in more detail. These drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of systems and methods that are consistent with aspects of the present application as detailed in the accompanying claims.
The transportation of the dangerous chemical vehicles is an important component part of a traffic system, and the effective monitoring and early warning of the transportation safety of the dangerous chemical vehicles also becomes an indispensable link for ensuring traffic safety and civil safety. Due to the proliferation of fake plate vehicle phenomenon and bus private use phenomenon, how to identify and effectively early warn dangerous chemical vehicles with the phenomenon becomes a hot spot.
In the prior art, the early warning of the dangerous chemical vehicle is realized based on a GPS system arranged on the dangerous chemical vehicle, the vehicle is intercepted by acquiring the vehicle position reported by the GPS system, combining with a manual card setting mode, predicting the vehicle running track according to the vehicle position and arranging a manual card along the way. Through the manual interception mode, the running state of the vehicle, the preset running track, the related information of the carried articles and the like can be checked, and the compliance and safety of the transportation of the vehicle are ensured.
However, in such a way, a large amount of human resources are required to be called for checking one by one on site, and particularly once the vehicle position reported by the GPS system is inaccurate, the accuracy of manual card setting can be seriously affected, the human resources are wasted, the early warning accuracy is reduced, and the traffic safety hidden trouble is easy to occur.
In order to solve the technical problems, the method and the device for detecting the vehicle sampling track have the advantages that track correction is carried out on the acquired vehicle sampling track so as to ensure correct vehicle track information based on early warning, the correction track and the preset track are used for determining the running error time of the vehicle when running tasks are executed, and further the running state of the vehicle is identified based on the running error time, and risk early warning is carried out.
Referring to fig. 1, fig. 1 is a schematic diagram of a network architecture according to the present application, where the network architecture shown in fig. 1 may specifically include a vehicle 1 and a server 2 for vehicle risk early warning.
The vehicle 1 may specifically be a vehicle that needs to perform a preset running task according to a preset track, including, but not limited to, dart vehicles, custody vehicles, logistics vehicles, and hazardous chemical substance transport vehicles. And particularly, the vehicle risk early warning provided by the application has a better early warning effect aiming at dangerous chemical transportation vehicles.
In addition, the server 2 for vehicle risk early warning may be specifically hardware carrying an early warning device, where the early warning device will adopt the early warning method provided by the application to early warn the running risk of the vehicle 1, where the server 2 may be specifically installed in a cloud server and interact with the vehicle 1 through a network. Meanwhile, when the server 2 performs running risk early warning on the vehicle, the early warning information can be sent to the vehicle 1 and also sent to a management terminal (not shown) of the traffic environment, so that drivers and related vehicle management personnel can sense the running risk of the vehicle.
Example 1
Fig. 2 is a flow chart of a vehicle driving risk early warning method provided in the present application, as shown in fig. 2, the method includes:
and 101, acquiring a sampling track when the vehicle runs according to a preset track, and carrying out track correction on the sampling track to obtain a corrected track of the vehicle.
It should be noted that, the execution main body of the early warning method for vehicle running risk provided in the present application is an early warning device, which may be specifically set in the electronic device on which the server is based.
In step 101, when the vehicle is running, the GPS system on the vehicle samples the current GPS position information of the vehicle according to the preset sampling frequency, and reports the current GPS position information to the early warning device. The early warning device can construct a sampling track of the vehicle based on the obtained GPS position information and the time information of the sampling moment for sampling the GPS position information. Wherein the sampling track can be specifically
It should be noted that, at the same time, there may be a plurality of vehicles in the traffic environment to simultaneously execute the driving task, when the vehicles synchronously upload the GPS position information, the early warning device may identify the attributions of the GPS position information one by one based on the vehicle identifier of the vehicle to which the vehicle belongs together with the GPS position information, and construct the sampling track of the vehicle by using the GPS position information belonging to the same vehicle according to the vehicle identifier.
Then, the early warning device will need to carry out track correction on the sampling track so as to obtain a corrected track of the vehicle. The track correction of the sampling track may specifically be implemented in various ways, including but not limited to: and sequentially segmenting the sampling track by combining road network information of the electronic map and matching the road network information so as to correct the position of each track in the sampling track and obtain a corrected track. It is known that the corrected trajectory should be the trajectory closest to the real running trajectory.
The following provides an optional implementation manner of step 101, through which the sampling track can be effectively corrected, so as to ensure a higher similarity between the corrected track obtained by correction and the real running track, specifically, the sampling track includes track positions of the vehicle at a plurality of sampling moments, in the optional implementation manner of step 101, the track correction is performed on the sampling track, and the track correction is performed on the sampling track according to each sampling moment relation between track positions to obtain the corrected track of the vehicle.
For example, fig. 3 is a schematic diagram of a track provided in this embodiment, as shown in fig. 3, the position coordinate of the track position b is on the mounting, but since the front and rear points (track position a and track position c) are located on the three-ring garden bridge, it can be inferred that the position coordinate of the track position b is greatly shifted, and the corrected position coordinate thereof should be the track position b' in the figure. In other words, for correction of the track position b, other track positions, such as the track position a and the track position c, should be referred to.
Further, since the relative position distances between the track positions and the differences between the sampling timings are different, when a certain track position is corrected, the correlation between the position coordinates of the other track positions and the position coordinates of the track position to be corrected is different. Generally, if the distance between the position coordinates of a certain track position and the track position to be corrected is smaller or the sampling time is closer, the certain track position will make more contribution in the process of correcting the coordinates of the track position to be corrected; conversely, if the distance between the position coordinates of a certain track position and the track position to be corrected is larger, or the sampling time interval is larger, the certain track position will make a smaller contribution in the process of correcting the coordinates of the track position to be corrected.
Still taking fig. 3 as an example, for the position coordinates of the track position b, the influence of the track position a and the track position c on b is much larger than the track position d, i.e., although the track position d is a leading point of the track position b in time series, for the problem of determining the corrected position of the track position b, the influence of the positions of the track position a and the track position c which are closer to the track position b in time distance should be larger.
With this feature, a more accurate correction can be made for the track position, namely:
and determining correction weights of other track positions in the sampling track relative to the track position to be corrected according to the track position to be corrected in the sampling track, wherein the correction weights are used for representing the association degree of the other track positions and the track position to be corrected. And carrying out coordinate correction on the position coordinates of the track position to be corrected according to the correction weights and the position coordinates of the other track positions, and taking the corrected position coordinates as corrected track positions. And finally, finishing the correction of each track position in the sampling track, wherein each corrected track position forms a correction track of the vehicle.
Alternatively, the acquisition of the correction weights may be based on a weight function, based on this embodiment. That is, the method may further include establishing a weight function for determining the correction weight; and determining the correction weight of other track positions in the sampling track relative to the track position to be corrected according to the weight function.
Specifically, for the weight function, it may be based on the existing smoothing function, and the Sigmoid function is taken as an example for the following description:
The Sigmoid function described above may be used to represent the weight w between track position i and track position j ij 。
Wherein, sigma is a smoothing coefficient between 0 and 1, which is a fixed value in actual use, generally, the larger sigma is, the more rapidly the influence degree of the track position j on the track position i is reduced; the smaller σ, the slower the influence of the track position j on the track position i decreases.
Wherein t is j And t i The sampling instants of track position i and track position j respectively,then the difference threshold for the sampling instant.
The method is characterized in that when the weight between the track position j and the track position i is determined, the difference between the acquisition moments can be determined, and the influence is determined according to the magnitude relation of the difference and the difference threshold value of the product of the smoothing coefficient and the sampling moment. Wherein when the product of the difference and the smoothing coefficient is greater than the difference threshold at the sampling instant, the +.>Taking 1; conversely, when the product of the difference and the smoothing coefficient is smaller than the difference threshold at the sampling instant, the +.>Get->
By the method, the correction weight of each track position can be effectively determined, so that accurate coordinate correction can be carried out on the track position to be corrected.
In addition, on the basis of the above embodiments, optionally, when the track positions are corrected, the sequence of sampling moments should be combined to ensure that the track positions obtained by sampling first are corrected. That is, during processing, the early warning device can sort the track positions according to the sampling time of each track position; and sequentially selecting the track position with the earliest sampling time from the uncorrected track positions according to the sequence as the track position to be corrected, so as to carry out coordinate correction of the position coordinates on the track position to be corrected.
In the above embodiments, optionally, when the track position to be corrected is corrected, the other corrected position used should be the latest track position, that is, if the track position has already been corrected, the corrected track position will be used in the present correction. That is, in the correcting method, the correcting method may further include: determining whether the position coordinates of each other track position are corrected; if yes, adopting the corrected position coordinates and the correction weight to carry out coordinate correction on the position coordinates of the track position to be corrected. In this way, the accuracy of the correction of the track position can be further improved.
After the acquisition of the corrected trajectory is completed, the early warning device will perform the following steps:
102, determining the vehicle running error time according to the corrected track of the vehicle and the preset track of the vehicle;
and 103, early warning the running risk of the vehicle according to the running error time of the vehicle.
In early warning of vehicle driving risk, common situations requiring early warning include, but are not limited to: fake plate vehicles and buses are private. A common feature, which is especially faced with both problems, is that the vehicle will not travel according to a preset travel path in the travel task, or that the vehicle will travel to a preset destination using the travel path. In either case, it may result in the vehicle not reaching the preset within the allowable range of the preset arrival time.
Therefore, the embodiment of the application determines whether the two conditions occur in the vehicle or not based on analysis of the running error time of the vehicle, and gives early warning when the corresponding conditions occur.
Specifically, the early warning device will determine the vehicle travel error time as described in step 102. In the embodiment of the present application, the determination of the vehicle driving error time may be performed based on two dimensions, that is, based on the arrival time and based on the time of passing the route point, and each mode will be described below separately:
the first implementation mode:
the early warning device determines the actual arrival time of the vehicle reaching a preset end point according to the corrected track of the vehicle; and determining the vehicle running error time according to the actual arrival time and the preset arrival time.
Specifically, the preset track is a track that the vehicle should travel in the driving task. Generally, due to the specificity of a vehicle, such as a dangerous chemical transportation vehicle, before the vehicle performs a driving task, a driving path of the dangerous chemical transportation vehicle needs to be reasonably planned based on an existing route planning mode, and a preset driving path obtained by planning is a preset track.
In this embodiment, in order to determine the vehicle running error time, the early warning device may acquire a preset track in a running task of the vehicle, and determine the vehicle running error time by combining a preset arrival time in the preset track and a preset terminal.
Of course, in order to determine the driving error time, the early warning device also needs to determine the actual arrival time of the vehicle. The actual arrival time is generally obtained by predicting the time when the current vehicle reaches the preset end point based on the corrected track of the current vehicle.
Alternatively, the following manner may be used in determining the actual arrival time: determining a current position of the vehicle in the corrected trajectory; and determining the running time required by the vehicle to run from the current position to a preset end point along the preset track according to the preset track, wherein the running time is taken as the actual arrival time of the vehicle to the preset end point.
Specifically, in this embodiment, the early warning device may first determine the current position of the vehicle in the corrected trajectory, which may be used to represent the current position information of the vehicle. Then, on the preset track, the driving time required for driving from the current position to a preset end point along the preset track is determined. And finally, combining the current time to determine the actual arrival time of the vehicle. It can be known that, in this embodiment, the actual arrival time should be updated in real time, that is, the actual arrival time should be updated once every time the current position of the vehicle is obtained, so that the early warning device performs early warning in time based on the relationship between the actual arrival time and the preset arrival time.
That is, in the actual processing, the early warning device may perform the steps of determining the actual arrival time of the vehicle to reach the preset destination according to the corrected track of the vehicle multiple times, and obtain multiple actual arrival times of the vehicle. When determining the vehicle running error time according to the actual arrival time and the preset arrival time, calculating the difference between the actual arrival time and the preset arrival time according to the actual arrival time of the vehicle obtained by each determination, and finally taking the accumulated sum value of the difference values as the vehicle running error time, thereby realizing the determination of the vehicle running error time.
On the basis of this embodiment, in one of the cases, the vehicle may have completely deviated from the original preset travel track, at this time, the early warning device may reconstruct the preset track based on the determination of whether the current position of the vehicle coincides with the preset travel route, and determine the actual arrival time based on the reconstructed preset track.
Specifically, the early warning device determines whether the vehicle is located on the preset track according to the current position;
when the vehicle deviates from the preset track, the early warning device needs to reconstruct the preset track according to the current position and the preset end point when the current position is not located in the preset track. Then, the early warning device determines the running time required by the vehicle to run from the current position to the preset end point along the reconstructed preset track according to the reconstructed preset track, wherein the running time is taken as the actual arrival time of the vehicle to the preset end point.
When the vehicle does not deviate from the preset track, the current position is located on the preset track, and at this time, the early warning device can directly execute the step of determining the running time required by the vehicle to run from the current position to the preset end point along the preset track according to the preset track, wherein the running time is taken as the actual arrival time of the vehicle reaching the preset end point.
For example, let the preset track beAnd the estimated time of arrival is denoted as et pre Time t of vehicle running error cet ，t cet Is 0; wherein the vehicle running error time is expressed asn is the total number of times of actual arrival time, deltat, counted during the running of the vehicle i Indicated at t i At the moment, the time difference between the actual arrival time and the estimated arrival time of the vehicle.
Let the correction track beThe optional early warning device can also add +.>And traj pre Comparing, if the track is corrected->For a preset track traj pre Then the vehicle is traveling in accordance with the preset trajectory.
Otherwise, if the track is correctedNot being a preset track traj pre If the vehicle is not travelling according to the preset trajectory, the warning device will then be based on +.>And a preset end point, re-planning the preset track of the vehicle to obtain a reconstructed track traj temp I.e. preset track traj pre traj temp At the same time, as described above, the actual arrival time is updated based on the new preset trajectory to obtain et temp . At this time, the vehicle running error time is updated to integrate the current updated time difference with the previous time difference, thereby obtaining t cet +et pre -et temp 。
If the running error time of the vehicle is greater than the accumulated error time threshold value theta set by the system cet (the time threshold can be set by the algorithm user himself, such as 30 minutes, 1 hour, etc.), the vehicle is given risk warning. Namely, the vehicle has a large possibility of being in non-compliance transportation and being a fake-licensed vehicle.
By the method, the actual reaching preset end time of the vehicle can be estimated to serve as the actual reaching time, the running time error of the vehicle is determined according to the difference between the actual reaching time and the preset reaching time of the expected reaching end, and when the error is larger than a certain error threshold, the vehicle can be indicated to possibly not execute running tasks according to a standard and safe running track, and early warning can be sent out.
The second implementation mode:
the early warning device determines the actual route time of the vehicle reaching each route point according to the corrected track of the vehicle;
And determining the vehicle running error time according to the actual route time and the preset route time of each route point.
Specifically, the preset track is a track that the vehicle should travel in the driving task, and the preset track includes a plurality of route points and corresponding preset route time. Generally, due to the specificity of a vehicle, such as a dangerous chemical transportation vehicle, before the vehicle performs a driving task, a driving path of the dangerous chemical transportation vehicle needs to be reasonably planned based on an existing route planning mode, and a preset driving path obtained by planning is a preset track.
In this embodiment, in order to determine the vehicle running error time, the early warning device may acquire a preset track in a running task of the vehicle, and determine the vehicle running error time by combining that the preset track includes a plurality of passing points and corresponding preset path times in the preset track.
Optionally, the early warning device determines the actual route time of the vehicle reaching each route point according to the corrected track of the vehicle at the moment of determining the running error of the vehicle; and determining the vehicle running error time according to the actual route time and the preset route time of each route point.
That is, in this embodiment, the time of arrival will not be referenced. The vehicle driving error time is the accumulated value of the approach time difference, and once the accumulated value is larger than the preset error threshold value, the vehicle is likely to be driven away from the preset driving track or an unexpected condition occurs, and the early warning device can perform early warning.
Furthermore, it should be noted that the two embodiments described above may also be combined to determine the vehicle travel error time, i.e., the sum of the path time differences and the arrival time differences is used as the vehicle travel error time. The specific implementation manner is similar to that of the foregoing embodiment, and will not be described herein.
Wherein, each technical detail is similar to the previous mode, and is not repeated here.
Fig. 4 is a flow chart of another early warning method for vehicle driving risk according to an embodiment of the present application, as shown in fig. 4, where on the basis of the foregoing embodiments, the method may include:
step 201, obtaining a sampling track when the vehicle runs according to a preset track, and carrying out track correction on the sampling track to obtain a corrected track of the vehicle.
Step 202, determining a geographical area through which the vehicle passes according to the corrected track of the vehicle;
and 203, determining the regional information of the geographical region through which the vehicle passes so as to early warn the running risk of the vehicle.
In this embodiment, step 201 can be specifically referred to the foregoing embodiment, and this embodiment will not be described in detail.
Specifically, the early warning device can determine the regional function distribution of the geographic region according to the regional information of the geographic region; determining risk coefficients of the geographic area according to the regional function distribution and a preset risk value corresponding to the regional function; and pre-warning the running risk of the vehicle according to the risk coefficient of the geographic area.
For example, the area information of the geographical area may be specifically represented by POI information, i.e., the area information represents an area function of the geographical area, such as belonging to a living business district, an agricultural land, an industrial land, or the like. For each geographic area, its area functions are typically complex, i.e., if 60% of the functions in a geographic area are business, 30% of the functions are educational, and the remaining 10% are residential.
Thus, the regional function distribution can be considered when determining the risk factor. For example, assuming that a geographic region may have m functions in total, the vector of its regional function distribution may be expressed as: w= [ W ] 1 ，w 2 ，...w j ，...，w m ]Wherein j represents the j-th region function, w j Representing the percentage weight, w, of the set geographical area on the j-th area function j ∈[0，1]，
At the same time, different regional functions will correspond to different risk values, which are generally defined according to the requirements of the driving task. For example, if a hazardous chemical passes through a living area where people are dense, the risk coefficient is high, and if a wide wasteland passes through, the risk coefficient is low, or the like. For example, the Risk value is set to risk= [ R 1 ，R 2 ，...，R j ，...，R m ]Wherein j represents the j-th region function of the m region functions, R j Representing the risk value of the j-th function.
To sum up, the final risk factor for a certain geographical area is expressed as
The early warning method for the vehicle running risk further carries out early warning on the vehicle running risk by obtaining the risk coefficient of the vehicle passing through the geographic area when running.
Example two
Corresponding to the early warning method of the vehicle driving risk in the above embodiment, fig. 5 is a schematic structural diagram of an early warning device provided in the embodiment of the present application, and as shown in fig. 5, the early warning device includes: the track processing module 10, the error time processing module 20 and the early warning module 30.
The track processing module 10 is configured to obtain a sampling track when the vehicle runs according to a preset track, and perform track correction on the sampling track to obtain a corrected track of the vehicle;
an error time processing module 20, configured to determine a vehicle running error time according to the corrected track of the vehicle and a preset track of the vehicle;
and the early warning module 30 is used for early warning the running risk of the vehicle according to the running error time of the vehicle.
In an alternative embodiment, the early warning device further includes: a region information processing module;
the regional information processing module is used for determining the geographical region through which the vehicle passes according to the corrected track of the vehicle, and determining regional information of the geographical region through which the vehicle passes so as to enable the early warning module to early warn the running risk of the vehicle.
Further, in other alternative embodiments, the sampling trajectory includes a trajectory position of the vehicle at a plurality of sampling instants;
the track processing module 10 is specifically configured to perform track correction on the sampling track according to each sampling time relationship between each track position, so as to obtain a corrected track of the vehicle.
In an alternative embodiment, the track processing module 10 is specifically configured to determine, for a track position to be corrected in the sampling track, a correction weight of other track positions in the sampling track relative to the track position to be corrected, where the correction weight is used to indicate a degree of association between the other track positions and the track position to be corrected; according to the correction weight and the position coordinates of each other track position, carrying out coordinate correction on the position coordinates of the track position to be corrected, and taking the corrected position coordinates as corrected track positions; and finishing the correction of each track position in the sampling track, wherein each corrected track position forms a correction track of the vehicle.
In an alternative embodiment, the track processing module 10 is specifically further configured to: establishing a weight function for determining the correction weight; and determining the correction weight of other track positions in the sampling track relative to the track position to be corrected according to the weight function.
In an alternative embodiment, the track processing module 10 is specifically configured to sort the track positions according to sampling moments of the track positions; and sequentially selecting the track position with the earliest sampling time from the uncorrected track positions according to the sequence as the track position to be corrected, so as to carry out coordinate correction of the position coordinates on the track position to be corrected.
In an alternative embodiment, the track processing module 10 is specifically further configured to determine whether the coordinate correction of the position coordinates is completed for each other track position; if yes, adopting the corrected position coordinates and the correction weight to carry out coordinate correction on the position coordinates of the track position to be corrected.
In an alternative embodiment, the preset track includes a preset endpoint and a preset arrival time;
the error time processing module 20 is specifically configured to determine an actual arrival time of the vehicle reaching a preset end point according to the corrected track of the vehicle;
and determining the vehicle running error time according to the actual arrival time and the preset arrival time.
In an alternative embodiment, the error time processing module 20 is further configured to perform the steps of determining an actual arrival time of the vehicle to reach the preset end point according to the corrected trajectory of the vehicle, and obtaining a plurality of actual arrival times of the vehicle;
The determining the vehicle driving error time according to the actual arrival time and the preset arrival time comprises the following steps:
calculating a difference between the actual arrival time and a preset arrival time for each determined actual arrival time of the vehicle;
the cumulative sum of the differences is taken as the vehicle running error time.
In an alternative embodiment, the error time processing module 20 is specifically configured to: determining a current position of the vehicle in the corrected trajectory; and determining the running time required by the vehicle to run from the current position to a preset end point along the preset track according to the preset track, wherein the running time is taken as the actual arrival time of the vehicle to the preset end point.
In an alternative embodiment, the error time processing module 20 is further configured to determine whether the vehicle is located on the preset track according to the current position;
if not, reconstructing a preset track according to the current position and a preset end point;
correspondingly, determining the running time required by the vehicle to run from the current position to a preset end point along the preset track according to the preset track, wherein the running time is taken as the actual arrival time of the vehicle to the preset end point, and comprises the following steps:
and determining the running time required by the vehicle to run from the current position to a preset end point along the reconstructed preset track according to the reconstructed preset track, wherein the running time is taken as the actual arrival time of the vehicle to the preset end point.
In an alternative embodiment, the preset track includes a plurality of route points and corresponding preset route time;
the error time processing module 20 is specifically configured to determine an actual route time of the vehicle reaching each route point according to the corrected track of the vehicle; and determining the vehicle running error time according to the actual route time and the preset route time of each route point.
In an alternative embodiment, the error time processing module 20 is specifically configured to determine a route time difference between the actual route time of each route point and each preset route time; and taking the accumulated value of the time differences of each route as the running error time of the vehicle.
In an alternative embodiment, the area information processing module is specifically configured to determine an area function distribution of a geographic area according to area information of the geographic area; determining risk coefficients of the geographic area according to the regional function distribution and a preset risk value corresponding to the regional function; and pre-warning the running risk of the vehicle according to the risk coefficient of the geographic area.
The early warning device for the vehicle running risk acquires a sampling track when the vehicle runs according to a preset track, and carries out track correction on the sampling track to acquire a corrected track of the vehicle; determining the vehicle running error time according to the corrected track of the vehicle and the preset track of the vehicle; and early warning is carried out on the vehicle running risk according to the vehicle running error time, so that the real running track of the vehicle can be obtained, the running error time is determined based on the real running track of the vehicle, the early warning of the vehicle running risk is carried out, the early warning accuracy is further effectively improved, and the safety of the traffic environment is ensured.
Example III
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 6, an embodiment of the present invention further provides an electronic device 1400, including: memory 1401, processor 1402 and computer program.
Wherein a computer program is stored in the memory 1401 and configured to be executed by the processor 1402 to implement the vehicle running risk pre-warning method provided by any of the embodiments of the present invention. The related descriptions and effects corresponding to the steps in the drawings can be understood correspondingly, and are not repeated here.
In this embodiment, the memory 1401 and the processor 1402 are connected via a bus.
Example IV
The embodiment of the invention provides a computer readable storage medium, on which a computer program is stored, the computer program being executed by a processor to implement the vehicle running risk early warning method provided by any one of the embodiments of the invention.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods may be implemented in other ways. For example, the system embodiments described above are merely illustrative, e.g., the division of modules is merely a logical function division, and there may be additional divisions of actual implementation, e.g., multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with respect to each other may be through some interface, indirect coupling or communication connection of systems or modules, electrical, mechanical, or other form.
The modules illustrated as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in hardware plus software functional modules.
Program code for carrying out methods of the present invention may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable warning device such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or any suitable combination of the preceding. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Moreover, although operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims.
Claims (13)
1. The early warning method for the running risk of the vehicle is characterized by comprising the following steps of:
acquiring a sampling track when a vehicle runs according to a preset track, and carrying out track correction on the sampling track to obtain a corrected track of the vehicle;
determining the vehicle running error time according to the corrected track of the vehicle and the preset track of the vehicle;
early warning is carried out on the running risk of the vehicle according to the running error time of the vehicle;
determining a geographical area through which the vehicle passes according to the corrected track of the vehicle;
determining regional information of a geographical region through which the vehicle passes so as to early warn the running risk of the vehicle;
the determining the regional information of the geographical region through which the vehicle passes to early warn the running risk of the vehicle comprises the following steps:
determining the regional function distribution of the geographic region according to the regional information of the geographic region;
Determining risk coefficients of the geographic area according to the regional function distribution and a preset risk value corresponding to the regional function;
early warning is carried out on the running risk of the vehicle according to the risk coefficient of the geographic area;
the sampling track comprises track positions of the vehicle at a plurality of sampling moments;
the track correction is performed on the sampling track to obtain a corrected track of the vehicle, and the method comprises the following steps:
carrying out track correction on the sampling tracks according to the sampling time relations among the track positions to obtain corrected tracks of the vehicle;
performing track correction on the sampling track according to each sampling time relation among track positions to obtain a corrected track of the vehicle, wherein the method comprises the following steps:
determining correction weights of other track positions in the sampling track relative to the track position to be corrected according to the track position to be corrected in the sampling track, wherein the correction weights are used for representing the association degree of the other track positions and the track position to be corrected;
according to the correction weight and the position coordinates of each other track position, carrying out coordinate correction on the position coordinates of the track position to be corrected, and taking the corrected position coordinates as corrected track positions;
And finishing the correction of each track position in the sampling track, wherein each corrected track position forms a correction track of the vehicle.
2. The method of claim 1, further comprising:
establishing a weight function for determining the correction weight;
and determining the correction weight of other track positions in the sampling track relative to the track position to be corrected according to the weight function.
3. The method of claim 1, further comprising:
sequencing the track positions according to the sampling time of the track positions;
and sequentially selecting the track position with the earliest sampling time from the uncorrected track positions according to the sequence as the track position to be corrected, so as to carry out coordinate correction of the position coordinates on the track position to be corrected.
4. The method according to claim 1, wherein the performing coordinate correction on the position coordinates of the track position to be corrected according to the correction weight and the position coordinates of each other track position further comprises:
determining whether the position coordinates of each other track position are corrected;
if yes, adopting the corrected position coordinates and the correction weight to carry out coordinate correction on the position coordinates of the track position to be corrected.
5. The method of claim 1, wherein the predetermined trajectory comprises a predetermined endpoint and a predetermined arrival time;
the determining the vehicle running error time according to the corrected track of the vehicle and the preset track of the vehicle comprises the following steps:
determining the actual arrival time of the vehicle reaching a preset end point according to the corrected track of the vehicle;
and determining the vehicle running error time according to the actual arrival time and the preset arrival time.
6. The method of claim 5, further comprising:
repeatedly executing the steps of determining the actual arrival time of the vehicle reaching a preset end point according to the corrected track of the vehicle, and obtaining a plurality of actual arrival times of the vehicle;
the determining the vehicle driving error time according to the actual arrival time and the preset arrival time comprises the following steps:
calculating a difference between the actual arrival time and a preset arrival time for each determined actual arrival time of the vehicle;
the cumulative sum of the differences is taken as the vehicle running error time.
7. The method of claim 6, wherein determining the actual arrival time of the vehicle at the preset destination according to the corrected trajectory of the vehicle comprises:
Determining a current position of the vehicle in the corrected trajectory;
and determining the running time required by the vehicle to run from the current position to a preset end point along the preset track according to the preset track, wherein the running time is taken as the actual arrival time of the vehicle to the preset end point.
8. The method of claim 7, further comprising:
determining whether the vehicle is positioned on the preset track according to the current position;
if not, reconstructing a preset track according to the current position and a preset end point;
correspondingly, determining the running time required by the vehicle to run from the current position to a preset end point along the preset track according to the preset track, wherein the running time is taken as the actual arrival time of the vehicle to the preset end point, and comprises the following steps:
and determining the running time required by the vehicle to run from the current position to a preset end point along the reconstructed preset track according to the reconstructed preset track, wherein the running time is taken as the actual arrival time of the vehicle to the preset end point.
9. The method of claim 1, wherein the predetermined trajectory includes a plurality of route points and corresponding predetermined route times;
The determining the vehicle running error time according to the corrected track of the vehicle and the preset track of the vehicle comprises the following steps:
determining the actual path time of the vehicle reaching each path point according to the corrected track of the vehicle;
and determining the vehicle running error time according to the actual route time and the preset route time of each route point.
10. The method of claim 9, wherein determining the vehicle travel error time based on the actual route time and the preset route time for each route point comprises:
determining the path time difference between the actual path time of each path point and each preset path time;
and taking the accumulated value of the time differences of each route as the running error time of the vehicle.
11. A vehicle travel risk early warning device, characterized by comprising:
the track processing module is used for acquiring a sampling track when the vehicle runs according to a preset track, and carrying out track correction on the sampling track to obtain a corrected track of the vehicle;
the error time processing module is used for determining the running error time of the vehicle according to the corrected track of the vehicle and the preset track of the vehicle;
the early warning module is used for early warning the running risk of the vehicle according to the running error time of the vehicle;
The regional information processing module is used for determining the geographical region through which the vehicle passes according to the corrected track of the vehicle; determining regional information of a geographical region through which the vehicle passes, so that the early warning module can early warn the running risk of the vehicle;
the regional information processing module is specifically used for determining regional function distribution of the geographic region according to regional information of the geographic region; determining risk coefficients of the geographic area according to the regional function distribution and a preset risk value corresponding to the regional function; early warning is carried out on the running risk of the vehicle according to the risk coefficient of the geographic area;
the sampling track comprises track positions of the vehicle at a plurality of sampling moments;
the track processing module is specifically configured to determine, for a track position to be corrected in a sampling track, a correction weight of other track positions in the sampling track relative to the track position to be corrected, where the correction weight is used to represent a degree of association between the other track positions and the track position to be corrected; according to the correction weight and the position coordinates of each other track position, carrying out coordinate correction on the position coordinates of the track position to be corrected, and taking the corrected position coordinates as corrected track positions; and finishing the correction of each track position in the sampling track, wherein each corrected track position forms a correction track of the vehicle.
12. An electronic device, comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing computer-executable instructions stored in the memory, causing the at least one processor to perform the pre-warning method of any one of claims 1-10.
13. A computer readable storage medium having stored therein computer executable instructions which, when executed by a processor, implement the pre-warning method of any one of claims 1 to 10.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105761490A (en) * | 2016-04-22 | 2016-07-13 | 北京国交信通科技发展有限公司 | Method of carrying out early warning on hazardous chemical substance transport vehicle parking in service area |
| CN105957387A (en) * | 2016-07-11 | 2016-09-21 | 南通大学 | Driving state early warning method of fixed route vehicle |
| WO2017157119A1 (en) * | 2016-03-18 | 2017-09-21 | 中兴通讯股份有限公司 | Method and device for identifying abnormal behavior of vehicle |
| CN107702727A (en) * | 2017-09-04 | 2018-02-16 | 武汉光庭科技有限公司 | Make the smooth device and method of vehicle location during a kind of automatic Pilot |
| CN109087506A (en) * | 2018-07-26 | 2018-12-25 | 东软集团股份有限公司 | A kind of vehicle monitoring method and device |
| CN111279215A (en) * | 2018-12-03 | 2020-06-12 | 深圳市大疆创新科技有限公司 | Target detection method and device, track management method and device and unmanned aerial vehicle |
-
2020
- 2020-06-29 CN CN202010605377.5A patent/CN113766426B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017157119A1 (en) * | 2016-03-18 | 2017-09-21 | 中兴通讯股份有限公司 | Method and device for identifying abnormal behavior of vehicle |
| CN105761490A (en) * | 2016-04-22 | 2016-07-13 | 北京国交信通科技发展有限公司 | Method of carrying out early warning on hazardous chemical substance transport vehicle parking in service area |
| CN105957387A (en) * | 2016-07-11 | 2016-09-21 | 南通大学 | Driving state early warning method of fixed route vehicle |
| CN107702727A (en) * | 2017-09-04 | 2018-02-16 | 武汉光庭科技有限公司 | Make the smooth device and method of vehicle location during a kind of automatic Pilot |
| CN109087506A (en) * | 2018-07-26 | 2018-12-25 | 东软集团股份有限公司 | A kind of vehicle monitoring method and device |
| CN111279215A (en) * | 2018-12-03 | 2020-06-12 | 深圳市大疆创新科技有限公司 | Target detection method and device, track management method and device and unmanned aerial vehicle |
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|---|---|
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